ional linkage derived from coevolu tion II. STRING: known and predicted protein-p rotein associations, inte grated and transferred across organisms Hoyoung Jeong
Jan 17, 2016
I. Prolinks: a database of protein functional linkage derived from coevolution
II. STRING: known and predicted protein-protein associations, integrated and transferred across organisms
Hoyoung Jeong
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Table Of Contents
Introduction Genomic Inference Method
Phylogenetic profile method Gene cluster method Gene neighbor method Rosetta Stone method
TextLinks Comparative benchmarking database
Prolinks STRING
System Proteome Navigator STRING
Conclusion
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Introduction(1/2)
Genome sequencing has allowed scientists to identify most of the genes encoded in each organism The function of many, typically 50%, of translated proteins
can be inferred from sequence comparison with previously characterized sequences
The assignment of function by homology gives only a partial understanding of a protein’s role within a cell
A more complete understanding of a protein function requires the identification of interacting partners
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Introduction(2/2)
Functional linkage Need the use of non-homology-based methods Two proteins are the components of a molecular complex and metab
olic pathway
Genomic inference method Phylogenetic profile method Gene neighbors method Rosetta stone method Gene cluster method These methods infer functional linkage between proteins by identifyin
g pairs of nonhomologous proteins that co-evolve
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Phylogenetic profile method(1/3)
Use the co-occurrence or absence of pairs of nonhomologous genes across genomes to infer functional relatedness We can define a homolog of a query protein to be present in a secon
dary genome, using BLAST N genomes yield an N-dimensional vector of ones and zeroes for the
query protein - phylogenetic profile
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Phylogenetic profile method(2/3)
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Phylogenetic profile method(3/3)
Using this approach, we can compute the phylogenetic profiles for each protein coded within a genome of interest
Need to determine the probability that two proteins have co-evolved We should compute the probability that two proteins have co-evolved by chance
P(k’|n,m,N) =
n N - nk m - k
Nm
• N represents the total # of genomes analyzed• n, the # of homologs for protein A• m, the # of homologs for protein B• k’, the # of genomes that contain homologs of both A and B
Because P represents the probability that the proteins do not co-evolve, 1-P(k > k’) is then the probability that they co-evolve
Hypergeometric ditribution
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Gene cluster method(1/2)
Within bacteria, protein of closely related function are often transcribed from a single functional unit known as an operon Operons contain two or more closely spaced genes located on the sa
me DNA strand Our approach to the identification of operons that gene start position
can be modeled by a Poisson distribution Unlike the other co-evolution methods, that is able to identify potenti
al functions for proteins exhibiting no homology to proteins in other genomes
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Gene cluster method(2/2)
P(start) = me-m P(N_positions_without_starts) = me-Nm
Where, m is the total # of genes divided by the # of intergenic nucleotides
The probability that two genes that are adjacent and coded on the same strand are part of an operon is 1-P
P(separation < N) = ∫ me-mN = 1-e-mx
x
0
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Gene neighbor method(1/2)
Some of the operons contained within a particular organism may be conserved across other organism That may provides additional evidence that the genes within the oper
on are functionally coupled And may be components of a molecular complex and metabolic path
way
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Gene neighbor method(2/2)
Our approach, first computes the probability that two genes are separated by fewer than d genes:
The likelihood of two genes is
P(≤d) = 2d
N - 1
Pm(≤X) = 1 – Pm(>X) ≈ X∑
m
i = 1
m-1
k = 0
(-lnX)k
k!where X = ∏ Pi(≤di), m is the # of organism that contain homologs of the two genes
Where, N is the total # of genes in the genome
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Rosetta Stone method(1/2)
Occasionally, two proteins expressed separately in one organism can be found as a single chain in the same or second genome It may the clue to infer functional relatedness of gene
fusion/division Proteins may carry out consecutive metabolic steps or are
components of molecular complex To detect gene-fusion events, we first align all protein-
coding sequences from a genome against the database using BLAST
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Rosetta Stone method(2/2)
We identify cases where two nonhomologous proteins both align over at least 70% of their sequence to different portions of a third protein
To screen out these confounding fusion, we compute the probability that two proteins are found by chance
P(k’|n,m,N) =
n N - nk m - k
Nm
Where k’ is the # of Rosetta Stone sequences
Therefore, the probability that two proteins have fused is given by 1 – P(k > k’)
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TextLinks(1/2)
Different from the methods above, is not a gene context analysis method The co-occurrence of gene names and symbols within the scientific litera
ture be used For this analysis, we have used the PubMed database, containing 14 mill
ion abstract and citations As with the phylogenetic profile method, abstracts and individual gene na
mes were used to develop a binary vector The result is an N-dimensional vector of ones and zeroes
Where, N is the total # of abstract Marked as one when a protein name is found within a given abstract or citati
on Marked as zero when a protein name is not found within a given abstract or c
itation
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TextLinks(2/2)
To protect a co-occurrence by chance, use a phylogenetic profile method
P(k’|n,m,N) =
n N - nk m - k
Nm
1 – P(k>k’)
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Comparative benchmarking database(1/3)
Database has Prolinks(2004)
83 genomes, 18,077,293 links between proteins STRING(2005)
730,000 proteins
Genomic inference method Prolinks
Phylogenetic profile, Gene neighbors, Rosetta stone, Gene cluster method TextLinks
STRING Phylogenetic profile, Gene neighbors, Rosetta stone method TextLinks, Experiments, Database, Textmining
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Comparative benchmarking database(2/3)
Prolinks STRING
Confidential metric Prolinks - COG(Clusters of Orthologous Groups) pathway STRING - KEGG(Kyoto Encyclopedia Genes and Genomes) pathway
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Comparative benchmarking database(3/3)
We have downloaded all the functional links for E. coli each database, we obtained(experimented on by Prolinks, 2004) # of Links
Prolinks - 515,892 links STRING - 407,520 links
Confidence Prolinks - 20% of the links between proteins assigned to a COG pathway STRING - 17% of the annotated links were between protein in the same
pathway
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Proteome Navigator
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Conclusion
Over the past few years significant progress has been made to protein interaction In spite of affluent data, biologists are still limited in their
coverage of organism The majority of protein interactions have been measured
within a single organism
The computational methodology may help them